2 research outputs found
Learning-Based Adaptive Transmission for Limited Feedback Multiuser MIMO-OFDM
Performing link adaptation in a multiantenna and multiuser system is
challenging because of the coupling between precoding, user selection, spatial
mode selection and use of limited feedback about the channel. The problem is
exacerbated by the difficulty of selecting the proper modulation and coding
scheme when using orthogonal frequency division multiplexing (OFDM). This paper
presents a data-driven approach to link adaptation for multiuser multiple input
mulitple output (MIMO) OFDM systems. A machine learning classifier is used to
select the modulation and coding scheme, taking as input the SNR values in the
different subcarriers and spatial streams. A new approximation is developed to
estimate the unknown interuser interference due to the use of limited feedback.
This approximation allows to obtain SNR information at the transmitter with a
minimum communication overhead. A greedy algorithm is used to perform spatial
mode and user selection with affordable complexity, without resorting to an
exhaustive search. The proposed adaptation is studied in the context of the
IEEE 802.11ac standard, and is shown to schedule users and adjust the
transmission parameters to the channel conditions as well as to the rate of the
feedback channel
Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is expected to require more effective and
efficient wireless communications than ever before. For this reason, techniques
such as spectrum sharing, dynamic spectrum access, extraction of signal
intelligence and optimized routing will soon become essential components of the
IoT wireless communication paradigm. Given that the majority of the IoT will be
composed of tiny, mobile, and energy-constrained devices, traditional
techniques based on a priori network optimization may not be suitable, since
(i) an accurate model of the environment may not be readily available in
practical scenarios; (ii) the computational requirements of traditional
optimization techniques may prove unbearable for IoT devices. To address the
above challenges, much research has been devoted to exploring the use of
machine learning to address problems in the IoT wireless communications domain.
This work provides a comprehensive survey of the state of the art in the
application of machine learning techniques to address key problems in IoT
wireless communications with an emphasis on its ad hoc networking aspect.
First, we present extensive background notions of machine learning techniques.
Then, by adopting a bottom-up approach, we examine existing work on machine
learning for the IoT at the physical, data-link and network layer of the
protocol stack. Thereafter, we discuss directions taken by the community
towards hardware implementation to ensure the feasibility of these techniques.
Additionally, before concluding, we also provide a brief discussion of the
application of machine learning in IoT beyond wireless communication. Finally,
each of these discussions is accompanied by a detailed analysis of the related
open problems and challenges.Comment: Ad Hoc Networks Journa